This chapter discussed the results of investigations aimed at applying a cooperative multi-sensor approach to enhance the acquisition, tracking, and discrimination of moving targets with low false alarm rate. Multiple radars are assumed to operate together in a non-interference limited manner. A three-fold approach was discussed: (1) applying multi-objective joint optimization algorithms to set limits on the operational parameters of the radars to preclude electromagnetic interference (EMI) based on the Transmission Hyperspace paradigm; (2) measuring and processing radar returns in a shared manner for target feature extraction based on waveform diversity techniques; and (3) employing feature-aided track/fusion algorithms to detect, discriminate, and track real targets from the adversary noise cloud. Computer simulations showed that with the help of simple signal amplitude features obtained from scattering cross section measurements using spatially and frequency diverse radars the overall sensor system can achieve a much better performance for data association and target tracking.